We are looking for an experienced Manager, Data Science to lead the development and delivery of advanced data science solutions across pricing optimization, customer segmentation, marketing analytics, Bayesian modelling, marketing mix modelling (MMM), causal inference, and optimization.
The role combines hands-on technical expertise with data science leadership, requiring the ability to lead and mentor data scientists, solve complex business problems, engage with clients, and translate analytical insights into measurable business outcomes.
The ideal candidate should have a strong foundation in statistics, machine learning, predictive modelling, and optimization, with hands-on experience in Python, SQL, Bayesian modelling, and cloud-based machine learning environments such as AWS SageMaker.
Key Responsibilities:
- Lead and mentor a team of data scientists and provide technical direction across multiple data science projects.
- Own the end-to-end delivery of data science solutions, from problem definition and exploratory analysis through modelling, validation, deployment, and business adoption.
- Develop and lead pricing optimization and price elasticity models to support revenue, profitability, and market-share objectives.
- Build customer segmentation and targeting models using techniques such as K-Means, Gaussian Mixture Models (GMM), and DBSCAN.
- Develop predictive and statistical models using Regression, Random Forest, Decision Trees, Support Vector Machines (SVM), and other machine learning techniques.
- Develop Bayesian regression and hierarchical Bayesian models using frameworks such as PyMC or Stan.
- Apply causal inference and experimentation techniques such as A/B testing, geo experiments, Difference-in-Differences, Synthetic Control, and Regression Discontinuity.
- Develop constrained and multi-objective optimization solutions for pricing, media allocation, and business decision-making using tools such as SciPy Optimize and CVXPY.
- Deploy and operationalize machine learning and statistical models using AWS SageMaker.
- Apply model explainability techniques such as SHAP to translate complex model outputs into actionable business insights.
- Work closely with clients and senior stakeholders to understand business problems, communicate analytical findings, and translate data science solutions into business recommendations.
- Establish best practices around model development, experimentation, documentation, version control, reproducibility, and model governance.
- Review models and analytical approaches developed by team members and ensure statistical and technical quality.
- Proactively identify business opportunities where advanced analytics and data science can create measurable value.
- 9+ years of hands-on experience in Data Science, Advanced Analytics, or a related quantitative field, with experience leading or mentoring data scientists.
- Experience in LLM and Agentic AI Engineering Systems with demonstrated delivery and client management is highly regarded.
- Strong programming skills in Python and experience with pandas, NumPy, and scikit-learn.
- Strong SQL skills, including joins, window functions, aggregations, and working with large datasets.
- Strong understanding of applied statistics, probability, regression, hypothesis testing, model evaluation, and statistical inference.
- Hands-on experience with: Bayesian Modelling & Optimization, K-Means, Gaussian Mixture Models (GMM), DBSCAN, Regression Modelling, Random Forest, Decision Trees, Support Vector Machines (SVM) and Other supervised and unsupervised machine learning techniques
- Strong experience in feature engineering, model selection, validation, tuning, and performance evaluation.
- Experience with SHAP or other model explainability techniques
- Experience with pricing optimization, price elasticity, revenue optimization, or related decision science problems.
- Experience with Marketing Mix Modelling (MMM), adstock, saturation, response curves, and media budget optimization is highly desirable.
- Experience with A/B testing, experimental design, causal inference, geo experiments, Difference-in-Differences, Synthetic Control, or Regression Discontinuity.
- Ability to evaluate causal relationships, quantify uncertainty, and translate experimental results into business recommendations.
- Hands-on experience with AWS, particularly AWS SageMaker for model development, deployment, monitoring, or experimentation.
- Experience in financial services, retail, media, marketing, loyalty, payments, or customer analytics is preferred.
- Master's degree in Data Science, Statistics, Mathematics, Economics, Computer Science, Engineering, or a related quantitative discipline preferred.
Thrive & Grow with Us:
- Competitive Salary: Your skills and contributions are highly valued here, and we make sure your salary reflects that, rewarding you fairly for the knowledge and experience you bring to the table.
- Dynamic Career Growth: Our vibrant environment offers you the opportunity to grow rapidly, providing the right tools, mentorship, and experiences to fast-track your career.
- Idea Tanks: Innovation lives here. Our "Idea Tanks" are your playground to pitch, experiment, and collaborate on ideas that can shape the future.
- Growth Chats: Dive into our casual "Growth Chats" where you can learn from the best—whether it's over lunch or during a laid-back session with peers, it's the perfect space to grow your skills.
- Snack Zone: Stay fuelled and inspired! In our Snack Zone, you'll find a variety of snacks to keep your energy high and ideas flowing.
- Recognition & Rewards: We believe great work deserves to be recognized. Expect regular Hive-Fives, shoutouts, and the chance to see your ideas come to life as part of our reward program.
- Fuel Your Growth Journey with Certifications: We're all about your growth! Enhance your expertise with company-sponsored certifications in AI, Data Science, Cloud, and Analytics technologies.